ERZA-DETR: A Deep Learning-Based Detection Transformer with Enhanced Relational-Zone Aggregation for WCE Lesion Detection

Wireless capsule endoscopy (WCE) plays a vital role in non-invasive screening of small intestinal lesions. However, the automated detection of lesions remains challenging due to low contrast, uneven illumination, and severe visual variability across images. Existing convolutional detectors rely heavily on manually designed anchors and post-processing, while end-to-end detection transformers developed for natural images exhibit limited adaptability to the complex texture and spectral characteristics of WCE data. To overcome these limitations, this study proposes a deep learning-based detection transformer with enhanced relational-zone aggregation for WCE lesion detection, termed ERZA-DETR, specifically tailored for WCE lesion detection. The framework integrates three complementary modules: a Dual-Band Adaptive Fourier Spectral module (DBFS) that recalibrates frequency responses to suppress illumination artifacts and highlight lesion boundaries; a Fused Dual-scale Gated Convolutional module (FD-gConv) that selectively fuses multi-scale texture features; and a Graph-Linked Embedding at Semantic Scales module (GLES) that preserves local topological relationships through coordinate-gated aggregation. Experimental evaluations on the SEE-AI small intestine dataset demonstrate that ERZA-DETR achieves a 3.2% improvement in mAP@50 and a 12.4% reduction in parameters compared with RT-DETRv2, achieving a superior balance between detection accuracy, computational efficiency, and clinical applicability.